Overview

Dataset statistics

Number of variables19
Number of observations11960
Missing cells407
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory145.0 B

Variable types

Numeric12
Boolean1
Text4
Categorical2

Alerts

danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudness and 4 other fieldsHigh correlation
loudness is highly overall correlated with energy and 3 other fieldsHigh correlation
speechiness is highly overall correlated with explicitHigh correlation
acousticness is highly overall correlated with energy and 2 other fieldsHigh correlation
instrumentalness is highly overall correlated with energy and 2 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
number_of_artists is highly overall correlated with energyHigh correlation
explicit is highly overall correlated with speechiness and 1 other fieldsHigh correlation
top_genre is highly overall correlated with explicit and 1 other fieldsHigh correlation
emotion is highly overall correlated with energy and 4 other fieldsHigh correlation
explicit is highly imbalanced (61.2%)Imbalance
number_of_artists has 121 (1.0%) missing valuesMissing
top_genre has 169 (1.4%) missing valuesMissing
duration_ms is highly skewed (γ1 = 45.71689973)Skewed
url has unique valuesUnique
instrumentalness has 2599 (21.7%) zerosZeros
popularity has 2536 (21.2%) zerosZeros

Reproduction

Analysis started2023-11-08 21:26:02.220925
Analysis finished2023-11-08 21:26:28.769867
Duration26.55 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct997
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47954994
Minimum0
Maximum8.375
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:28.967887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.161
Q10.338
median0.485
Q30.608
95-th percentile0.761
Maximum8.375
Range8.375
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.27384517
Coefficient of variation (CV)0.57104619
Kurtosis301.73745
Mean0.47954994
Median Absolute Deviation (MAD)0.134
Skewness12.654747
Sum5735.4173
Variance0.074991175
MonotonicityNot monotonic
2023-11-08T22:26:29.197149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.562 35
 
0.3%
0.446 35
 
0.3%
0.551 35
 
0.3%
0.486 35
 
0.3%
0.514 35
 
0.3%
0.565 34
 
0.3%
0.485 34
 
0.3%
0.537 34
 
0.3%
0.538 33
 
0.3%
0.577 33
 
0.3%
Other values (987) 11617
97.1%
ValueCountFrequency (%)
0 6
0.1%
0.0567 1
 
< 0.1%
0.0584 1
 
< 0.1%
0.0589 1
 
< 0.1%
0.0596 1
 
< 0.1%
0.0598 1
 
< 0.1%
0.0607 2
 
< 0.1%
0.061 1
 
< 0.1%
0.0613 1
 
< 0.1%
0.0615 1
 
< 0.1%
ValueCountFrequency (%)
8.375 1
< 0.1%
7.764 1
< 0.1%
7.706 1
< 0.1%
7.494 1
< 0.1%
6.751 1
< 0.1%
6.632 1
< 0.1%
6.618 1
< 0.1%
6.539 1
< 0.1%
6.425 1
< 0.1%
5.463 1
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct1907
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52978252
Minimum0.000197
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:29.426157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.000197
5-th percentile0.0224
Q10.186
median0.595
Q30.841
95-th percentile0.965
Maximum1
Range0.999803
Interquartile range (IQR)0.655

Descriptive statistics

Standard deviation0.33307113
Coefficient of variation (CV)0.62869407
Kurtosis-1.4392826
Mean0.52978252
Median Absolute Deviation (MAD)0.295
Skewness-0.22656341
Sum6336.199
Variance0.11093638
MonotonicityNot monotonic
2023-11-08T22:26:29.653383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.941 32
 
0.3%
0.964 31
 
0.3%
0.961 31
 
0.3%
0.978 30
 
0.3%
0.957 29
 
0.2%
0.932 28
 
0.2%
0.917 28
 
0.2%
0.935 28
 
0.2%
0.877 28
 
0.2%
0.875 28
 
0.2%
Other values (1897) 11667
97.6%
ValueCountFrequency (%)
0.000197 1
< 0.1%
0.000594 1
< 0.1%
0.000655 1
< 0.1%
0.000685 1
< 0.1%
0.000724 1
< 0.1%
0.000795 1
< 0.1%
0.000882 1
< 0.1%
0.000919 1
< 0.1%
0.000923 1
< 0.1%
0.000977 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.999 3
 
< 0.1%
0.998 4
 
< 0.1%
0.997 8
 
0.1%
0.996 9
 
0.1%
0.995 24
0.2%
0.994 20
0.2%
0.993 18
0.2%
0.992 11
0.1%
0.991 12
0.1%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct9221
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-12.372094
Minimum-47.046
Maximum1.519
Zeros0
Zeros (%)0.0%
Negative11957
Negative (%)> 99.9%
Memory size93.6 KiB
2023-11-08T22:26:29.866790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-47.046
5-th percentile-28.63015
Q1-17.865
median-9.537
Q3-5.834
95-th percentile-3.3439
Maximum1.519
Range48.565
Interquartile range (IQR)12.031

Descriptive statistics

Standard deviation8.2650122
Coefficient of variation (CV)-0.66803666
Kurtosis0.1078043
Mean-12.372094
Median Absolute Deviation (MAD)4.666
Skewness-0.9663512
Sum-147970.24
Variance68.310426
MonotonicityNot monotonic
2023-11-08T22:26:30.083002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.399 6
 
0.1%
-6.196 5
 
< 0.1%
-5.325 5
 
< 0.1%
-8.609 5
 
< 0.1%
-6.209 5
 
< 0.1%
-5.268 5
 
< 0.1%
-5.627 5
 
< 0.1%
-5.1 5
 
< 0.1%
-6.89 5
 
< 0.1%
-4.876 5
 
< 0.1%
Other values (9211) 11909
99.6%
ValueCountFrequency (%)
-47.046 1
< 0.1%
-46.084 1
< 0.1%
-44.255 1
< 0.1%
-42.958 1
< 0.1%
-42.874 1
< 0.1%
-42.721 1
< 0.1%
-42.597 1
< 0.1%
-42.528 1
< 0.1%
-42.236 1
< 0.1%
-42.018 1
< 0.1%
ValueCountFrequency (%)
1.519 1
< 0.1%
0.918 1
< 0.1%
0.878 1
< 0.1%
-0.116 1
< 0.1%
-0.256 1
< 0.1%
-0.508 1
< 0.1%
-0.514 1
< 0.1%
-0.577 1
< 0.1%
-0.589 1
< 0.1%
-0.602 1
< 0.1%

speechiness
Real number (ℝ)

HIGH CORRELATION 

Distinct1210
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10432898
Minimum0
Maximum0.965
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:30.308364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.029
Q10.0373
median0.0459
Q30.0701
95-th percentile0.56035
Maximum0.965
Range0.965
Interquartile range (IQR)0.0328

Descriptive statistics

Standard deviation0.19166401
Coefficient of variation (CV)1.8371119
Kurtosis12.986428
Mean0.10432898
Median Absolute Deviation (MAD)0.0115
Skewness3.7586791
Sum1247.7746
Variance0.036735092
MonotonicityNot monotonic
2023-11-08T22:26:30.738585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0406 50
 
0.4%
0.0423 47
 
0.4%
0.041 47
 
0.4%
0.0447 46
 
0.4%
0.0416 46
 
0.4%
0.0436 45
 
0.4%
0.0402 45
 
0.4%
0.039 44
 
0.4%
0.0388 44
 
0.4%
0.0417 44
 
0.4%
Other values (1200) 11502
96.2%
ValueCountFrequency (%)
0 6
0.1%
0.0226 1
 
< 0.1%
0.0228 1
 
< 0.1%
0.0229 1
 
< 0.1%
0.023 1
 
< 0.1%
0.0232 2
 
< 0.1%
0.0233 1
 
< 0.1%
0.0235 5
< 0.1%
0.0236 1
 
< 0.1%
0.0237 1
 
< 0.1%
ValueCountFrequency (%)
0.965 1
 
< 0.1%
0.963 1
 
< 0.1%
0.962 1
 
< 0.1%
0.961 3
 
< 0.1%
0.96 4
 
< 0.1%
0.959 4
 
< 0.1%
0.958 2
 
< 0.1%
0.957 3
 
< 0.1%
0.956 9
0.1%
0.955 10
0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct3014
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48360493
Minimum0
Maximum0.996
Zeros29
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:30.958343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000148
Q10.0365
median0.468
Q30.923
95-th percentile0.992
Maximum0.996
Range0.996
Interquartile range (IQR)0.8865

Descriptive statistics

Standard deviation0.40450588
Coefficient of variation (CV)0.8364387
Kurtosis-1.7341465
Mean0.48360493
Median Absolute Deviation (MAD)0.4403
Skewness0.023119226
Sum5783.915
Variance0.16362501
MonotonicityNot monotonic
2023-11-08T22:26:31.181584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 208
 
1.7%
0.994 176
 
1.5%
0.993 168
 
1.4%
0.992 144
 
1.2%
0.991 109
 
0.9%
0.99 106
 
0.9%
0.988 96
 
0.8%
0.989 72
 
0.6%
0.984 68
 
0.6%
0.987 68
 
0.6%
Other values (3004) 10745
89.8%
ValueCountFrequency (%)
0 29
0.2%
1.01 × 10-61
 
< 0.1%
1.1 × 10-61
 
< 0.1%
1.26 × 10-61
 
< 0.1%
1.35 × 10-61
 
< 0.1%
1.38 × 10-61
 
< 0.1%
1.41 × 10-62
 
< 0.1%
1.44 × 10-61
 
< 0.1%
1.48 × 10-61
 
< 0.1%
1.61 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 43
 
0.4%
0.995 208
1.7%
0.994 176
1.5%
0.993 168
1.4%
0.992 144
1.2%
0.991 109
0.9%
0.99 106
0.9%
0.989 72
 
0.6%
0.988 96
0.8%
0.987 68
 
0.6%

instrumentalness
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3498
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26498631
Minimum0
Maximum0.994
Zeros2599
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:31.408576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.7 × 10-6
median0.00267
Q30.67825
95-th percentile0.932
Maximum0.994
Range0.994
Interquartile range (IQR)0.6782473

Descriptive statistics

Standard deviation0.37788476
Coefficient of variation (CV)1.4260539
Kurtosis-0.99578828
Mean0.26498631
Median Absolute Deviation (MAD)0.00267
Skewness0.91703649
Sum3169.2363
Variance0.14279689
MonotonicityNot monotonic
2023-11-08T22:26:31.655935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2599
 
21.7%
0.922 35
 
0.3%
0.902 32
 
0.3%
0.909 29
 
0.2%
0.919 29
 
0.2%
0.912 29
 
0.2%
0.933 29
 
0.2%
0.92 28
 
0.2%
0.903 28
 
0.2%
0.877 27
 
0.2%
Other values (3488) 9095
76.0%
ValueCountFrequency (%)
0 2599
21.7%
1 × 10-61
 
< 0.1%
1.01 × 10-63
 
< 0.1%
1.02 × 10-64
 
< 0.1%
1.03 × 10-65
 
< 0.1%
1.04 × 10-65
 
< 0.1%
1.05 × 10-69
 
0.1%
1.06 × 10-63
 
< 0.1%
1.07 × 10-62
 
< 0.1%
1.08 × 10-63
 
< 0.1%
ValueCountFrequency (%)
0.994 1
 
< 0.1%
0.993 1
 
< 0.1%
0.991 1
 
< 0.1%
0.99 2
 
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.986 1
 
< 0.1%
0.984 2
 
< 0.1%
0.983 6
0.1%
0.982 4
< 0.1%

liveness
Real number (ℝ)

Distinct1471
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22438263
Minimum0.00967
Maximum0.997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:31.880960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.00967
5-th percentile0.062495
Q10.0958
median0.13
Q30.28
95-th percentile0.772
Maximum0.997
Range0.98733
Interquartile range (IQR)0.1842

Descriptive statistics

Standard deviation0.21239207
Coefficient of variation (CV)0.94656198
Kurtosis3.3894049
Mean0.22438263
Median Absolute Deviation (MAD)0.0509
Skewness2.0018516
Sum2683.6163
Variance0.04511039
MonotonicityNot monotonic
2023-11-08T22:26:32.109698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 140
 
1.2%
0.111 118
 
1.0%
0.107 115
 
1.0%
0.108 115
 
1.0%
0.112 112
 
0.9%
0.104 111
 
0.9%
0.106 109
 
0.9%
0.102 106
 
0.9%
0.103 106
 
0.9%
0.101 104
 
0.9%
Other values (1461) 10824
90.5%
ValueCountFrequency (%)
0.00967 1
< 0.1%
0.017 1
< 0.1%
0.0185 1
< 0.1%
0.0195 1
< 0.1%
0.0199 1
< 0.1%
0.0207 1
< 0.1%
0.0218 1
< 0.1%
0.0224 1
< 0.1%
0.0245 1
< 0.1%
0.0246 1
< 0.1%
ValueCountFrequency (%)
0.997 1
 
< 0.1%
0.99 2
 
< 0.1%
0.988 2
 
< 0.1%
0.987 1
 
< 0.1%
0.986 1
 
< 0.1%
0.985 5
< 0.1%
0.984 2
 
< 0.1%
0.983 1
 
< 0.1%
0.982 5
< 0.1%
0.981 2
 
< 0.1%

valence
Real number (ℝ)

HIGH CORRELATION 

Distinct1470
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46131167
Minimum0
Maximum0.995
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:32.326696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0393
Q10.221
median0.461
Q30.68825
95-th percentile0.91
Maximum0.995
Range0.995
Interquartile range (IQR)0.46725

Descriptive statistics

Standard deviation0.27621879
Coefficient of variation (CV)0.59876827
Kurtosis-1.1368762
Mean0.46131167
Median Absolute Deviation (MAD)0.234
Skewness0.064219289
Sum5517.2876
Variance0.07629682
MonotonicityNot monotonic
2023-11-08T22:26:32.558099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.964 32
 
0.3%
0.961 28
 
0.2%
0.637 25
 
0.2%
0.639 24
 
0.2%
0.963 24
 
0.2%
0.65 23
 
0.2%
0.478 23
 
0.2%
0.638 23
 
0.2%
0.962 22
 
0.2%
0.592 22
 
0.2%
Other values (1460) 11714
97.9%
ValueCountFrequency (%)
0 6
0.1%
1 × 10-57
0.1%
0.0104 1
 
< 0.1%
0.0105 1
 
< 0.1%
0.0112 1
 
< 0.1%
0.0134 1
 
< 0.1%
0.0198 1
 
< 0.1%
0.0233 1
 
< 0.1%
0.0234 1
 
< 0.1%
0.0242 1
 
< 0.1%
ValueCountFrequency (%)
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.989 1
 
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.986 1
 
< 0.1%
0.982 2
< 0.1%
0.981 1
 
< 0.1%
0.979 4
< 0.1%
0.977 3
< 0.1%

tempo
Real number (ℝ)

Distinct11082
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.74986
Minimum0
Maximum241.423
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:32.806068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile71.0738
Q192.77525
median116.1535
Q3137.52525
95-th percentile176.02605
Maximum241.423
Range241.423
Interquartile range (IQR)44.75

Descriptive statistics

Standard deviation32.029332
Coefficient of variation (CV)0.27201164
Kurtosis-0.33364366
Mean117.74986
Median Absolute Deviation (MAD)22.4145
Skewness0.37512062
Sum1408288.3
Variance1025.8781
MonotonicityNot monotonic
2023-11-08T22:26:33.116512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
0.1%
100.002 5
 
< 0.1%
119.928 4
 
< 0.1%
86.064 4
 
< 0.1%
101.999 4
 
< 0.1%
120.004 4
 
< 0.1%
129.963 4
 
< 0.1%
110.006 3
 
< 0.1%
124.247 3
 
< 0.1%
119.991 3
 
< 0.1%
Other values (11072) 11920
99.7%
ValueCountFrequency (%)
0 6
0.1%
31.988 1
 
< 0.1%
32.451 1
 
< 0.1%
35.311 1
 
< 0.1%
38.815 1
 
< 0.1%
43.267 1
 
< 0.1%
43.903 1
 
< 0.1%
44.197 1
 
< 0.1%
44.887 1
 
< 0.1%
45.084 1
 
< 0.1%
ValueCountFrequency (%)
241.423 1
< 0.1%
215.918 1
< 0.1%
213.99 1
< 0.1%
211.014 1
< 0.1%
210.893 1
< 0.1%
209.727 1
< 0.1%
209.681 1
< 0.1%
209.68 1
< 0.1%
209.54 1
< 0.1%
208.675 1
< 0.1%

duration_ms
Real number (ℝ)

SKEWED 

Distinct9827
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1094073.4
Minimum-427346
Maximum1.9308213 × 109
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)0.1%
Memory size93.6 KiB
2023-11-08T22:26:33.390515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-427346
5-th percentile100454.15
Q1169209.75
median213453
Q3273933
95-th percentile460418
Maximum1.9308213 × 109
Range1.9312486 × 109
Interquartile range (IQR)104723.25

Descriptive statistics

Standard deviation33217750
Coefficient of variation (CV)30.361537
Kurtosis2255.8592
Mean1094073.4
Median Absolute Deviation (MAD)50309
Skewness45.7169
Sum1.3085118 × 1010
Variance1.1034189 × 1015
MonotonicityNot monotonic
2023-11-08T22:26:33.676358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166000 7
 
0.1%
159000 6
 
0.1%
255000 6
 
0.1%
185000 6
 
0.1%
188000 6
 
0.1%
156000 6
 
0.1%
176000 6
 
0.1%
170000 6
 
0.1%
205706 5
 
< 0.1%
244000 5
 
< 0.1%
Other values (9817) 11901
99.5%
ValueCountFrequency (%)
-427346 1
< 0.1%
-359471 1
< 0.1%
-296213 1
< 0.1%
-289266 1
< 0.1%
-275800 1
< 0.1%
-237360 1
< 0.1%
-234893 1
< 0.1%
-223426 1
< 0.1%
-217783 1
< 0.1%
-213672 1
< 0.1%
ValueCountFrequency (%)
1930821300 1
< 0.1%
1760220200 1
< 0.1%
1498900000 1
< 0.1%
1256662800 1
< 0.1%
1113920000 1
< 0.1%
655429100 1
< 0.1%
536983200 1
< 0.1%
500976000 1
< 0.1%
428151600 1
< 0.1%
356252400 1
< 0.1%

popularity
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)0.7%
Missing117
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean25.131892
Minimum0
Maximum82
Zeros2536
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:33.931388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median24
Q342
95-th percentile61
Maximum82
Range82
Interquartile range (IQR)39

Descriptive statistics

Standard deviation21.072017
Coefficient of variation (CV)0.83845723
Kurtosis-1.1065944
Mean25.131892
Median Absolute Deviation (MAD)20
Skewness0.30760299
Sum297637
Variance444.02989
MonotonicityNot monotonic
2023-11-08T22:26:34.203357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2536
 
21.2%
2 218
 
1.8%
1 201
 
1.7%
39 191
 
1.6%
44 189
 
1.6%
37 186
 
1.6%
36 186
 
1.6%
35 184
 
1.5%
40 182
 
1.5%
3 181
 
1.5%
Other values (73) 7589
63.5%
ValueCountFrequency (%)
0 2536
21.2%
1 201
 
1.7%
2 218
 
1.8%
3 181
 
1.5%
4 160
 
1.3%
5 165
 
1.4%
6 137
 
1.1%
7 115
 
1.0%
8 116
 
1.0%
9 153
 
1.3%
ValueCountFrequency (%)
82 1
 
< 0.1%
81 5
 
< 0.1%
80 3
 
< 0.1%
79 5
 
< 0.1%
78 3
 
< 0.1%
77 6
 
0.1%
76 14
0.1%
75 12
0.1%
74 9
0.1%
73 16
0.1%

number_of_artists
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)0.1%
Missing121
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1.7147563
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:34.465358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum19
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2763768
Coefficient of variation (CV)0.74434879
Kurtosis18.614128
Mean1.7147563
Median Absolute Deviation (MAD)0
Skewness3.2290804
Sum20301
Variance1.6291377
MonotonicityNot monotonic
2023-11-08T22:26:34.712092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 7463
62.4%
2 2217
 
18.5%
3 1173
 
9.8%
4 583
 
4.9%
5 176
 
1.5%
6 126
 
1.1%
7 33
 
0.3%
8 23
 
0.2%
9 14
 
0.1%
10 9
 
0.1%
Other values (6) 22
 
0.2%
(Missing) 121
 
1.0%
ValueCountFrequency (%)
1 7463
62.4%
2 2217
 
18.5%
3 1173
 
9.8%
4 583
 
4.9%
5 176
 
1.5%
6 126
 
1.1%
7 33
 
0.3%
8 23
 
0.2%
9 14
 
0.1%
10 9
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 2
 
< 0.1%
14 4
 
< 0.1%
13 4
 
< 0.1%
12 4
 
< 0.1%
11 7
 
0.1%
10 9
 
0.1%
9 14
0.1%
8 23
0.2%
7 33
0.3%

explicit
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
False
11052 
True
 
908
ValueCountFrequency (%)
False 11052
92.4%
True 908
 
7.6%
2023-11-08T22:26:34.896725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

name
Text

Distinct11500
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:35.273475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length174
Median length126
Mean length28.839047
Min length1

Characters and Unicode

Total characters344915
Distinct characters494
Distinct categories16 ?
Distinct scripts9 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11118 ?
Unique (%)93.0%

Sample

1st rowFantasy - Felix Jaehn Remix
2nd rowTurning Me Up (Hadal Ahbek)
3rd rowProblems
4th rowCloser (feat. Lilly Ahlberg)
5th rowEverything's Gonna Be Alright
ValueCountFrequency (%)
2527
 
3.9%
in 1889
 
2.9%
the 1564
 
2.4%
no 1285
 
2.0%
i 879
 
1.4%
major 803
 
1.2%
a 726
 
1.1%
op 709
 
1.1%
for 573
 
0.9%
1 570
 
0.9%
Other values (10942) 53133
82.2%
2023-11-08T22:26:35.934813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52698
 
15.3%
e 26591
 
7.7%
o 21362
 
6.2%
a 18705
 
5.4%
n 17691
 
5.1%
i 17095
 
5.0%
r 16166
 
4.7%
t 14552
 
4.2%
l 10322
 
3.0%
s 10097
 
2.9%
Other values (484) 139636
40.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 205492
59.6%
Uppercase Letter 53567
 
15.5%
Space Separator 52698
 
15.3%
Other Punctuation 15791
 
4.6%
Decimal Number 11320
 
3.3%
Dash Punctuation 2425
 
0.7%
Open Punctuation 1228
 
0.4%
Close Punctuation 1223
 
0.4%
Other Letter 998
 
0.3%
Final Punctuation 68
 
< 0.1%
Other values (6) 105
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
35
 
3.5%
27
 
2.7%
22
 
2.2%
19
 
1.9%
19
 
1.9%
19
 
1.9%
16
 
1.6%
15
 
1.5%
14
 
1.4%
13
 
1.3%
Other values (324) 799
80.1%
Lowercase Letter
ValueCountFrequency (%)
e 26591
12.9%
o 21362
10.4%
a 18705
9.1%
n 17691
 
8.6%
i 17095
 
8.3%
r 16166
 
7.9%
t 14552
 
7.1%
l 10322
 
5.0%
s 10097
 
4.9%
u 7076
 
3.4%
Other values (50) 45835
22.3%
Uppercase Letter
ValueCountFrequency (%)
I 4539
 
8.5%
S 4148
 
7.7%
M 4131
 
7.7%
A 3658
 
6.8%
T 3383
 
6.3%
B 3067
 
5.7%
C 2740
 
5.1%
L 2684
 
5.0%
R 2417
 
4.5%
D 2355
 
4.4%
Other values (23) 20445
38.2%
Other Punctuation
ValueCountFrequency (%)
. 5523
35.0%
, 3653
23.1%
: 3082
19.5%
' 1411
 
8.9%
" 1019
 
6.5%
/ 551
 
3.5%
& 252
 
1.6%
! 101
 
0.6%
? 90
 
0.6%
; 60
 
0.4%
Other values (9) 49
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 2343
20.7%
2 1791
15.8%
0 1365
12.1%
3 1079
9.5%
4 857
 
7.6%
5 854
 
7.5%
6 847
 
7.5%
8 772
 
6.8%
9 762
 
6.7%
7 650
 
5.7%
Math Symbol
ValueCountFrequency (%)
+ 14
50.0%
= 5
 
17.9%
> 3
 
10.7%
< 3
 
10.7%
~ 2
 
7.1%
1
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 2402
99.1%
13
 
0.5%
6
 
0.2%
2
 
0.1%
2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1175
95.7%
[ 51
 
4.2%
1
 
0.1%
1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 1170
95.7%
] 51
 
4.2%
1
 
0.1%
1
 
0.1%
Modifier Letter
ValueCountFrequency (%)
36
92.3%
ʻ 1
 
2.6%
ʼ 1
 
2.6%
1
 
2.6%
Nonspacing Mark
ValueCountFrequency (%)
̈ 1
25.0%
1
25.0%
̀ 1
25.0%
́ 1
25.0%
Final Punctuation
ValueCountFrequency (%)
53
77.9%
14
 
20.6%
» 1
 
1.5%
Initial Punctuation
ValueCountFrequency (%)
14
51.9%
12
44.4%
« 1
 
3.7%
Other Symbol
ValueCountFrequency (%)
° 2
50.0%
1
25.0%
® 1
25.0%
Space Separator
ValueCountFrequency (%)
52698
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 259057
75.1%
Common 84853
 
24.6%
Katakana 428
 
0.1%
Han 306
 
0.1%
Hiragana 258
 
0.1%
Hangul 7
 
< 0.1%
Inherited 4
 
< 0.1%
Greek 1
 
< 0.1%
Cyrillic 1
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
7
 
2.3%
6
 
2.0%
6
 
2.0%
6
 
2.0%
5
 
1.6%
5
 
1.6%
5
 
1.6%
4
 
1.3%
4
 
1.3%
4
 
1.3%
Other values (199) 254
83.0%
Latin
ValueCountFrequency (%)
e 26591
 
10.3%
o 21362
 
8.2%
a 18705
 
7.2%
n 17691
 
6.8%
i 17095
 
6.6%
r 16166
 
6.2%
t 14552
 
5.6%
l 10322
 
4.0%
s 10097
 
3.9%
u 7076
 
2.7%
Other values (81) 99400
38.4%
Katakana
ValueCountFrequency (%)
27
 
6.3%
22
 
5.1%
19
 
4.4%
19
 
4.4%
16
 
3.7%
15
 
3.5%
14
 
3.3%
13
 
3.0%
13
 
3.0%
12
 
2.8%
Other values (56) 258
60.3%
Common
ValueCountFrequency (%)
52698
62.1%
. 5523
 
6.5%
, 3653
 
4.3%
: 3082
 
3.6%
- 2402
 
2.8%
1 2343
 
2.8%
2 1791
 
2.1%
' 1411
 
1.7%
0 1365
 
1.6%
( 1175
 
1.4%
Other values (52) 9410
 
11.1%
Hiragana
ValueCountFrequency (%)
35
 
13.6%
19
 
7.4%
11
 
4.3%
11
 
4.3%
11
 
4.3%
10
 
3.9%
9
 
3.5%
8
 
3.1%
8
 
3.1%
7
 
2.7%
Other values (43) 129
50.0%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Inherited
ValueCountFrequency (%)
̈ 1
25.0%
1
25.0%
̀ 1
25.0%
́ 1
25.0%
Greek
ValueCountFrequency (%)
β 1
100.0%
Cyrillic
ValueCountFrequency (%)
Я 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 342935
99.4%
None 813
 
0.2%
Katakana 469
 
0.1%
CJK 305
 
0.1%
Hiragana 259
 
0.1%
Punctuation 119
 
< 0.1%
Hangul 7
 
< 0.1%
Diacriticals 3
 
< 0.1%
Modifier Letters 2
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52698
 
15.4%
e 26591
 
7.8%
o 21362
 
6.2%
a 18705
 
5.5%
n 17691
 
5.2%
i 17095
 
5.0%
r 16166
 
4.7%
t 14552
 
4.2%
l 10322
 
3.0%
s 10097
 
2.9%
Other values (78) 137656
40.1%
None
ValueCountFrequency (%)
é 329
40.5%
è 122
 
15.0%
ü 47
 
5.8%
ö 31
 
3.8%
ä 30
 
3.7%
à 27
 
3.3%
ã 24
 
3.0%
ê 21
 
2.6%
á 17
 
2.1%
ì 16
 
2.0%
Other values (43) 149
18.3%
Punctuation
ValueCountFrequency (%)
53
44.5%
14
 
11.8%
14
 
11.8%
13
 
10.9%
12
 
10.1%
6
 
5.0%
5
 
4.2%
2
 
1.7%
Katakana
ValueCountFrequency (%)
36
 
7.7%
27
 
5.8%
22
 
4.7%
19
 
4.1%
19
 
4.1%
16
 
3.4%
15
 
3.2%
14
 
3.0%
13
 
2.8%
13
 
2.8%
Other values (58) 275
58.6%
Hiragana
ValueCountFrequency (%)
35
 
13.5%
19
 
7.3%
11
 
4.2%
11
 
4.2%
11
 
4.2%
10
 
3.9%
9
 
3.5%
8
 
3.1%
8
 
3.1%
7
 
2.7%
Other values (44) 130
50.2%
CJK
ValueCountFrequency (%)
7
 
2.3%
6
 
2.0%
6
 
2.0%
6
 
2.0%
5
 
1.6%
5
 
1.6%
5
 
1.6%
4
 
1.3%
4
 
1.3%
4
 
1.3%
Other values (198) 253
83.0%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
50.0%
ʼ 1
50.0%
Misc Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Diacriticals
ValueCountFrequency (%)
̈ 1
33.3%
̀ 1
33.3%
́ 1
33.3%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Cyrillic
ValueCountFrequency (%)
Я 1
100.0%

url
Text

UNIQUE 

Distinct11960
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:36.238702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length53
Median length53
Mean length53
Min length53

Characters and Unicode

Total characters633880
Distinct characters65
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11960 ?
Unique (%)100.0%

Sample

1st rowhttps://open.spotify.com/track/7KCWmFdw0TzoJbKtqRRzJO
2nd rowhttps://open.spotify.com/track/4sofJiicQwKsRo3S1vpt39
3rd rowhttps://open.spotify.com/track/2dyrLrVopYKGc3H9oOYSmZ
4th rowhttps://open.spotify.com/track/5FDdviWQzhw7NWH2TiDl9d
5th rowhttps://open.spotify.com/track/2NEBP8SXEqwZt2PRdPXXuy
ValueCountFrequency (%)
https://open.spotify.com/track/7kcwmfdw0tzojbktqrrzjo 1
 
< 0.1%
https://open.spotify.com/track/3ngcatigvh77ddwainp996 1
 
< 0.1%
https://open.spotify.com/track/4shgvm4clrzediflnjwlgd 1
 
< 0.1%
https://open.spotify.com/track/0mdmnww0rpbjqj4swq0qbg 1
 
< 0.1%
https://open.spotify.com/track/7bnx2fgb4mjprybkhacnwv 1
 
< 0.1%
https://open.spotify.com/track/2dyrlrvopykgc3h9ooysmz 1
 
< 0.1%
https://open.spotify.com/track/5fddviwqzhw7nwh2tidl9d 1
 
< 0.1%
https://open.spotify.com/track/2nebp8sxeqwzt2prdpxxuy 1
 
< 0.1%
https://open.spotify.com/track/69dr48pxzsabfufqfddjdb 1
 
< 0.1%
https://open.spotify.com/track/5pncjn4ofnvlrfrzbhfqwh 1
 
< 0.1%
Other values (11950) 11950
99.9%
2023-11-08T22:26:36.850098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 51947
 
8.2%
/ 47840
 
7.5%
o 39999
 
6.3%
p 39986
 
6.3%
s 27956
 
4.4%
c 27924
 
4.4%
. 23920
 
3.8%
k 16133
 
2.5%
i 16131
 
2.5%
h 16114
 
2.5%
Other values (55) 325930
51.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 392791
62.0%
Uppercase Letter 105291
 
16.6%
Other Punctuation 83720
 
13.2%
Decimal Number 52078
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 51947
13.2%
o 39999
 
10.2%
p 39986
 
10.2%
s 27956
 
7.1%
c 27924
 
7.1%
k 16133
 
4.1%
i 16131
 
4.1%
h 16114
 
4.1%
y 16056
 
4.1%
m 16054
 
4.1%
Other values (16) 124491
31.7%
Uppercase Letter
ValueCountFrequency (%)
D 4231
 
4.0%
E 4151
 
3.9%
P 4139
 
3.9%
G 4130
 
3.9%
C 4122
 
3.9%
X 4110
 
3.9%
B 4090
 
3.9%
Z 4074
 
3.9%
O 4073
 
3.9%
L 4072
 
3.9%
Other values (16) 64099
60.9%
Decimal Number
ValueCountFrequency (%)
6 5621
10.8%
4 5594
10.7%
1 5554
10.7%
3 5544
10.6%
0 5533
10.6%
5 5528
10.6%
2 5463
10.5%
7 5178
9.9%
9 4041
7.8%
8 4022
7.7%
Other Punctuation
ValueCountFrequency (%)
/ 47840
57.1%
. 23920
28.6%
: 11960
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 498082
78.6%
Common 135798
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 51947
 
10.4%
o 39999
 
8.0%
p 39986
 
8.0%
s 27956
 
5.6%
c 27924
 
5.6%
k 16133
 
3.2%
i 16131
 
3.2%
h 16114
 
3.2%
y 16056
 
3.2%
m 16054
 
3.2%
Other values (42) 229782
46.1%
Common
ValueCountFrequency (%)
/ 47840
35.2%
. 23920
17.6%
: 11960
 
8.8%
6 5621
 
4.1%
4 5594
 
4.1%
1 5554
 
4.1%
3 5544
 
4.1%
0 5533
 
4.1%
5 5528
 
4.1%
2 5463
 
4.0%
Other values (3) 13241
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 633880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 51947
 
8.2%
/ 47840
 
7.5%
o 39999
 
6.3%
p 39986
 
6.3%
s 27956
 
4.4%
c 27924
 
4.4%
. 23920
 
3.8%
k 16133
 
2.5%
i 16131
 
2.5%
h 16114
 
2.5%
Other values (55) 325930
51.4%

genres
Text

Distinct3602
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:37.207111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length844
Median length239
Mean length84.992726
Min length7

Characters and Unicode

Total characters1016513
Distinct characters40
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2204 ?
Unique (%)18.4%

Sample

1st row['tropical house', 'german dance', 'pop dance', 'edm', 'vapor twitch', 'etherpop']
2nd row['pop dance', 'edm', 'indonesian viral pop', 'canadian hip hop']
3rd row['progressive electro house', 'progressive house', 'future house', 'chill house', 'pop edm', 'singer-songwriter pop', 'electro house', 'pop dance', 'dutch house', 'edm']
4th row['uk dance', 'tech house', 'pop dance', 'scandipop', 'house']
5th row['country road', 'country', 'contemporary country']
ValueCountFrequency (%)
rock 9100
 
8.2%
classical 7839
 
7.1%
blues 6855
 
6.2%
country 4577
 
4.1%
pop 2897
 
2.6%
baroque 2439
 
2.2%
punk 2142
 
1.9%
performance 2098
 
1.9%
metal 2022
 
1.8%
early 1964
 
1.8%
Other values (957) 68591
62.1%
2023-11-08T22:26:37.822829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 126143
 
12.4%
98564
 
9.7%
a 73349
 
7.2%
r 63987
 
6.3%
e 63703
 
6.3%
o 58916
 
5.8%
c 57512
 
5.7%
s 52019
 
5.1%
, 51121
 
5.0%
l 49406
 
4.9%
Other values (30) 321793
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 713255
70.2%
Other Punctuation 177523
 
17.5%
Space Separator 98564
 
9.7%
Close Punctuation 11960
 
1.2%
Open Punctuation 11960
 
1.2%
Dash Punctuation 3170
 
0.3%
Decimal Number 77
 
< 0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 73349
10.3%
r 63987
 
9.0%
e 63703
 
8.9%
o 58916
 
8.3%
c 57512
 
8.1%
s 52019
 
7.3%
l 49406
 
6.9%
i 43469
 
6.1%
n 42243
 
5.9%
t 35928
 
5.0%
Other values (16) 172723
24.2%
Other Punctuation
ValueCountFrequency (%)
' 126143
71.1%
, 51121
28.8%
& 144
 
0.1%
: 59
 
< 0.1%
" 56
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 38
49.4%
8 29
37.7%
1 9
 
11.7%
5 1
 
1.3%
Space Separator
ValueCountFrequency (%)
98564
100.0%
Close Punctuation
ValueCountFrequency (%)
] 11960
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 11960
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3170
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 713255
70.2%
Common 303258
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 73349
10.3%
r 63987
 
9.0%
e 63703
 
8.9%
o 58916
 
8.3%
c 57512
 
8.1%
s 52019
 
7.3%
l 49406
 
6.9%
i 43469
 
6.1%
n 42243
 
5.9%
t 35928
 
5.0%
Other values (16) 172723
24.2%
Common
ValueCountFrequency (%)
' 126143
41.6%
98564
32.5%
, 51121
16.9%
] 11960
 
3.9%
[ 11960
 
3.9%
- 3170
 
1.0%
& 144
 
< 0.1%
: 59
 
< 0.1%
" 56
 
< 0.1%
2 38
 
< 0.1%
Other values (4) 43
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1016513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 126143
 
12.4%
98564
 
9.7%
a 73349
 
7.2%
r 63987
 
6.3%
e 63703
 
6.3%
o 58916
 
5.8%
c 57512
 
5.7%
s 52019
 
5.1%
, 51121
 
5.0%
l 49406
 
4.9%
Other values (30) 321793
31.7%
Distinct180
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size93.6 KiB
2023-11-08T22:26:38.054399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length48
Median length40
Mean length11.525585
Min length7

Characters and Unicode

Total characters137846
Distinct characters28
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)0.4%

Sample

1st row['edm']
2nd row['edm']
3rd row['edm']
4th row['house']
5th row['country']
ValueCountFrequency (%)
classical 3030
22.4%
rock 1640
12.1%
blues 1175
 
8.7%
country 1088
 
8.0%
punk 714
 
5.3%
comedy 602
 
4.4%
pop 532
 
3.9%
bluegrass 527
 
3.9%
folk 355
 
2.6%
grunge 308
 
2.3%
Other values (40) 3567
26.3%
2023-11-08T22:26:38.703716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 27076
19.6%
[ 11960
 
8.7%
] 11960
 
8.7%
c 10227
 
7.4%
l 9477
 
6.9%
s 9270
 
6.7%
a 8413
 
6.1%
o 6347
 
4.6%
e 5710
 
4.1%
r 5088
 
3.7%
Other values (18) 32318
23.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83317
60.4%
Other Punctuation 28654
 
20.8%
Open Punctuation 11960
 
8.7%
Close Punctuation 11960
 
8.7%
Space Separator 1578
 
1.1%
Dash Punctuation 377
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 10227
12.3%
l 9477
11.4%
s 9270
11.1%
a 8413
10.1%
o 6347
 
7.6%
e 5710
 
6.9%
r 5088
 
6.1%
u 4329
 
5.2%
i 3736
 
4.5%
k 3180
 
3.8%
Other values (12) 17540
21.1%
Other Punctuation
ValueCountFrequency (%)
' 27076
94.5%
, 1578
 
5.5%
Open Punctuation
ValueCountFrequency (%)
[ 11960
100.0%
Close Punctuation
ValueCountFrequency (%)
] 11960
100.0%
Space Separator
ValueCountFrequency (%)
1578
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83317
60.4%
Common 54529
39.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 10227
12.3%
l 9477
11.4%
s 9270
11.1%
a 8413
10.1%
o 6347
 
7.6%
e 5710
 
6.9%
r 5088
 
6.1%
u 4329
 
5.2%
i 3736
 
4.5%
k 3180
 
3.8%
Other values (12) 17540
21.1%
Common
ValueCountFrequency (%)
' 27076
49.7%
[ 11960
21.9%
] 11960
21.9%
, 1578
 
2.9%
1578
 
2.9%
- 377
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 27076
19.6%
[ 11960
 
8.7%
] 11960
 
8.7%
c 10227
 
7.4%
l 9477
 
6.9%
s 9270
 
6.7%
a 8413
 
6.1%
o 6347
 
4.6%
e 5710
 
4.1%
r 5088
 
3.7%
Other values (18) 32318
23.4%

top_genre
Categorical

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)0.3%
Missing169
Missing (%)1.4%
Memory size93.6 KiB
classical
3030 
rock
1640 
blues
1122 
country
1082 
punk
636 
Other values (27)
4281 

Length

Max length10
Median length9
Mean length6.3120176
Min length3

Characters and Unicode

Total characters74425
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowedm
2nd rowedm
3rd rowedm
4th rowhouse
5th rowcountry

Common Values

ValueCountFrequency (%)
classical 3030
25.3%
rock 1640
13.7%
blues 1122
 
9.4%
country 1082
 
9.0%
punk 636
 
5.3%
comedy 599
 
5.0%
bluegrass 511
 
4.3%
pop 482
 
4.0%
sleep 294
 
2.5%
folk 246
 
2.1%
Other values (22) 2149
18.0%

Length

2023-11-08T22:26:38.925354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
classical 3030
25.7%
rock 1640
13.9%
blues 1122
 
9.5%
country 1082
 
9.2%
punk 636
 
5.4%
comedy 599
 
5.1%
bluegrass 511
 
4.3%
pop 482
 
4.1%
sleep 294
 
2.5%
folk 246
 
2.1%
Other values (22) 2149
18.2%

Most occurring characters

ValueCountFrequency (%)
c 10001
13.4%
l 8852
11.9%
s 8840
11.9%
a 7635
10.3%
o 5460
 
7.3%
e 4574
 
6.1%
r 4375
 
5.9%
u 3735
 
5.0%
i 3580
 
4.8%
k 2820
 
3.8%
Other values (13) 14553
19.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74160
99.6%
Dash Punctuation 265
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 10001
13.5%
l 8852
11.9%
s 8840
11.9%
a 7635
10.3%
o 5460
 
7.4%
e 4574
 
6.2%
r 4375
 
5.9%
u 3735
 
5.0%
i 3580
 
4.8%
k 2820
 
3.8%
Other values (12) 14288
19.3%
Dash Punctuation
ValueCountFrequency (%)
- 265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74160
99.6%
Common 265
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 10001
13.5%
l 8852
11.9%
s 8840
11.9%
a 7635
10.3%
o 5460
 
7.4%
e 4574
 
6.2%
r 4375
 
5.9%
u 3735
 
5.0%
i 3580
 
4.8%
k 2820
 
3.8%
Other values (12) 14288
19.3%
Common
ValueCountFrequency (%)
- 265
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 10001
13.4%
l 8852
11.9%
s 8840
11.9%
a 7635
10.3%
o 5460
 
7.3%
e 4574
 
6.1%
r 4375
 
5.9%
u 3735
 
5.0%
i 3580
 
4.8%
k 2820
 
3.8%
Other values (13) 14553
19.6%

emotion
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.6 KiB
happy
3451 
sad
2953 
energetic
2815 
calm
2741 

Length

Max length9
Median length5
Mean length5.2184783
Min length3

Characters and Unicode

Total characters62413
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhappy
2nd rowhappy
3rd rowenergetic
4th rowhappy
5th rowhappy

Common Values

ValueCountFrequency (%)
happy 3451
28.9%
sad 2953
24.7%
energetic 2815
23.5%
calm 2741
22.9%

Length

2023-11-08T22:26:39.122715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T22:26:39.286740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
happy 3451
28.9%
sad 2953
24.7%
energetic 2815
23.5%
calm 2741
22.9%

Most occurring characters

ValueCountFrequency (%)
a 9145
14.7%
e 8445
13.5%
p 6902
11.1%
c 5556
 
8.9%
h 3451
 
5.5%
y 3451
 
5.5%
s 2953
 
4.7%
d 2953
 
4.7%
n 2815
 
4.5%
r 2815
 
4.5%
Other values (5) 13927
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62413
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9145
14.7%
e 8445
13.5%
p 6902
11.1%
c 5556
 
8.9%
h 3451
 
5.5%
y 3451
 
5.5%
s 2953
 
4.7%
d 2953
 
4.7%
n 2815
 
4.5%
r 2815
 
4.5%
Other values (5) 13927
22.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 62413
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9145
14.7%
e 8445
13.5%
p 6902
11.1%
c 5556
 
8.9%
h 3451
 
5.5%
y 3451
 
5.5%
s 2953
 
4.7%
d 2953
 
4.7%
n 2815
 
4.5%
r 2815
 
4.5%
Other values (5) 13927
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9145
14.7%
e 8445
13.5%
p 6902
11.1%
c 5556
 
8.9%
h 3451
 
5.5%
y 3451
 
5.5%
s 2953
 
4.7%
d 2953
 
4.7%
n 2815
 
4.5%
r 2815
 
4.5%
Other values (5) 13927
22.3%

Interactions

2023-11-08T22:26:25.925147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:04.516594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:06.339838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:08.201109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:10.274161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:12.543339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:14.418618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:16.285096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:18.137004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:20.015134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:22.080253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:24.108661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:26.085151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:04.655587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:06.494841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:08.345109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:10.453161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:12.705322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:14.568651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:16.427058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:18.288152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:20.164132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:22.244274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:24.258671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:26.243147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:04.805586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:06.650839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:08.496105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:10.646158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:12.856311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:14.719618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:16.579064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:18.438128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:20.323131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:22.410016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:24.404659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:26.402146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:04.948586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:06.796117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:08.640133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:10.807974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:13.007311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:14.870622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:16.724069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:18.586125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:20.477136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:22.572008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:24.558046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:26.564526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:05.098817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:06.947111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:08.797450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:10.970939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:13.161087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:15.024626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:16.879704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:18.744121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:20.630130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:22.735047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:24.699081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:26.721507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:05.251305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:07.102113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:09.108310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:11.135967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:13.311078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:15.176623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:17.031727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:18.891428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:20.784968image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:22.900014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:24.854046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:26.885508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:05.406848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:07.251359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:09.256711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:11.320942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:13.463080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:15.333629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:17.187732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:19.046431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:21.145282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:23.070014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:24.996647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:27.041669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:05.563867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:07.404355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:09.408715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:11.522938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:13.617080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:15.488747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:17.339733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:19.219410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:21.298284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:23.246043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:25.141674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:27.202226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:05.710852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:07.561339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:09.562741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:11.726971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:13.778112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:15.639730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:17.492729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:19.374677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:21.451842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:23.417475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:25.296657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:27.366240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:05.859839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:07.718563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:09.708704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:11.941134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:13.927085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:15.790730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:17.649732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:19.531676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:21.597843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:23.581475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:25.459152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:27.546640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:06.034873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:07.892557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:09.896966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:12.180140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:14.108008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:15.973032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:17.827009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:19.706674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:21.771864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:23.766479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:25.634180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:27.699455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:06.175837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:08.041565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:10.097996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:12.360345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:14.258034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:16.123089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:17.975994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:19.852680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:21.921476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:23.928661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T22:26:25.767182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-08T22:26:39.433123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
danceabilityenergyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mspopularitynumber_of_artistsexplicittop_genreemotion
danceability1.0000.2990.3930.021-0.249-0.4250.0580.6050.079-0.1580.139-0.3240.0640.0970.081
energy0.2991.0000.8790.345-0.807-0.5040.3250.3830.320-0.0680.194-0.5330.2470.3520.636
loudness0.3930.8791.0000.206-0.765-0.5930.2440.4030.314-0.0530.267-0.4880.2020.3320.547
speechiness0.0210.3450.2061.000-0.152-0.1830.212-0.0610.075-0.066-0.066-0.0800.5590.3550.187
acousticness-0.249-0.807-0.765-0.1521.0000.414-0.158-0.333-0.333-0.005-0.3020.4930.2220.3660.573
instrumentalness-0.425-0.504-0.593-0.1830.4141.000-0.266-0.342-0.1520.127-0.1400.4020.1760.2550.510
liveness0.0580.3250.2440.212-0.158-0.2661.0000.0990.057-0.084-0.066-0.2010.3640.2460.190
valence0.6050.3830.403-0.061-0.333-0.3420.0991.0000.275-0.2790.113-0.3530.1400.2470.364
tempo0.0790.3200.3140.075-0.333-0.1520.0570.2751.000-0.0610.115-0.2050.0560.1600.221
duration_ms-0.158-0.068-0.053-0.066-0.0050.127-0.084-0.279-0.0611.0000.0230.0760.0000.0150.014
popularity0.1390.1940.267-0.066-0.302-0.140-0.0660.1130.1150.0231.000-0.1270.1380.2300.189
number_of_artists-0.324-0.533-0.488-0.0800.4930.402-0.201-0.353-0.2050.076-0.1271.0000.0700.2000.223
explicit0.0640.2470.2020.5590.2220.1760.3640.1400.0560.0000.1380.0701.0000.6000.213
top_genre0.0970.3520.3320.3550.3660.2550.2460.2470.1600.0150.2300.2000.6001.0000.596
emotion0.0810.6360.5470.1870.5730.5100.1900.3640.2210.0140.1890.2230.2130.5961.000

Missing values

2023-11-08T22:26:27.956484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-08T22:26:28.382833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-08T22:26:28.654835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

danceabilityenergyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mspopularitynumber_of_artistsexplicitnameurlgenresfiltered_genrestop_genreemotion
00.6380.781-6.8480.02850.01180.0095300.34900.250122.985194640.034.03.0FalseFantasy - Felix Jaehn Remixhttps://open.spotify.com/track/7KCWmFdw0TzoJbKtqRRzJO['tropical house', 'german dance', 'pop dance', 'edm', 'vapor twitch', 'etherpop']['edm']edmhappy
10.7440.816-3.9330.03720.14400.0000090.09020.748120.062144000.052.03.0FalseTurning Me Up (Hadal Ahbek)https://open.spotify.com/track/4sofJiicQwKsRo3S1vpt39['pop dance', 'edm', 'indonesian viral pop', 'canadian hip hop']['edm']edmhappy
20.7210.768-4.3710.06850.21000.0084300.26200.468124.076179032.045.03.0TrueProblemshttps://open.spotify.com/track/2dyrLrVopYKGc3H9oOYSmZ['progressive electro house', 'progressive house', 'future house', 'chill house', 'pop edm', 'singer-songwriter pop', 'electro house', 'pop dance', 'dutch house', 'edm']['edm']edmenergetic
30.7000.782-4.6310.05420.44900.0000000.14300.680124.969169901.046.03.0FalseCloser (feat. Lilly Ahlberg)https://open.spotify.com/track/5FDdviWQzhw7NWH2TiDl9d['uk dance', 'tech house', 'pop dance', 'scandipop', 'house']['house']househappy
40.7640.587-7.0020.02980.07210.0000060.09400.49489.980229573.045.02.0FalseEverything's Gonna Be Alrighthttps://open.spotify.com/track/2NEBP8SXEqwZt2PRdPXXuy['country road', 'country', 'contemporary country']['country']countryhappy
50.5450.818-6.3800.05410.03690.0000000.10100.679109.019225066.067.01.0FalseMake Me Wannahttps://open.spotify.com/track/69DR48pXzSabFUfQfDDJDb['country road', 'modern country rock', 'country', 'contemporary country']['country']countryenergetic
60.6110.782-4.9890.04740.14700.0000000.11500.915101.319188053.00.01.0FalseCastawayhttps://open.spotify.com/track/5PNcJn4oFNvlRfrZBHfqWh['country road', 'modern country rock', 'country', 'contemporary country']['country']countryhappy
70.5570.737-5.7500.04760.01420.0000000.31400.817158.050204186.060.01.0FalseWhere It's Athttps://open.spotify.com/track/4whYDpJ5XVQpmvecbEHP5Q['country road', 'modern country rock', 'country', 'contemporary country']['country']countryhappy
80.7110.696-5.0500.08690.16000.0000000.17000.69480.512189533.063.01.0FalseGood Girlhttps://open.spotify.com/track/3NgcaTIgVh77DdwAInp996['country road', 'modern country rock', 'country', 'contemporary country']['country']countryhappy
90.4760.952-3.7830.04100.09810.0000030.43800.849170.062184706.064.01.0FalseSun Dazehttps://open.spotify.com/track/0El2Zyt68nYySFDG87hZgM['country', 'country pop', 'contemporary country', 'country road', 'modern country rock']['country']countryenergetic
danceabilityenergyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mspopularitynumber_of_artistsexplicitnameurlgenresfiltered_genrestop_genreemotion
119500.5290.284-10.7720.04020.8430.0000000.14600.687127.053178106.00.01.0FalseI'd Still Want You - First Version (With Yodel)https://open.spotify.com/track/1CcGzRkzta0Fzu68zqueFA['honky tonk', 'yodeling', 'country', 'traditional country']['country']countrysad
119510.7840.316-12.2390.06540.8920.0000000.13700.962107.704152906.00.01.0FalseI'm Satisfied With Youhttps://open.spotify.com/track/63ZxC0f1wXeK4Z7BqHbIbF['honky tonk', 'yodeling', 'country', 'traditional country']['country']countrysad
119520.5800.469-10.6830.03730.7770.0000050.16900.922177.757161693.00.01.0FalseThe Blues Come Aroundhttps://open.spotify.com/track/0EpTgU1NpffUHReE1PW3Rs['honky tonk', 'yodeling', 'country', 'traditional country']['country']countrysad
119530.6550.379-10.5590.03720.8710.0000000.17000.682130.281175666.00.01.0FalseYou're Gonna Change (Or I'm Gonna Leave) - Single Versionhttps://open.spotify.com/track/5MvfiJrn4yE7Tvu9EBI1Ma['honky tonk', 'yodeling', 'country', 'traditional country']['country']countrysad
119540.7660.358-10.3010.04170.7450.0000000.06240.964155.196164266.00.01.0FalseI Won't Be Home No Morehttps://open.spotify.com/track/6E76zA6qT1nPMOCD6ApkDw['honky tonk', 'yodeling', 'country', 'traditional country']['country']countrysad
119550.4420.379-12.7110.02640.9610.0005670.28400.65979.948123733.00.01.0FalseBlue Love (In My Heart) - Single Versionhttps://open.spotify.com/track/4GGCcixhaENoUig3n7M5s6['honky tonk', 'yodeling', 'country', 'traditional country']['country']countrysad
119560.4850.226-10.5030.02780.9820.0000000.09600.508139.543154160.00.01.0FalseMy Son Calls Another Man Daddy - Polydor Single Versionhttps://open.spotify.com/track/7iHxsTIBod6jhy9a205h3H['honky tonk', 'yodeling', 'country', 'traditional country']['country']countrysad
119570.7410.338-8.4880.04320.9300.0000010.16400.804133.504155626.00.01.0FalseI'd Still Want You - Single Versionhttps://open.spotify.com/track/5TXZYm7ScYmrFzPI3WOWKW['honky tonk', 'yodeling', 'country', 'traditional country']['country']countrysad
119580.4930.773-5.8750.04520.4560.0000030.10700.478140.998204640.00.01.0FalseMoon Over Mexicohttps://open.spotify.com/track/54QmQd9Nc2EUDha6dD8V9X['country', 'contemporary country']['country']countrysad
119590.7020.792-8.6310.03250.3400.0000810.17200.786123.377209400.040.01.0FalseWorkin' Man's Ph.Dhttps://open.spotify.com/track/1jfCCgyDtmZb53GrPhmAxr['country road', 'country', 'contemporary country']['country']countryhappy